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3D reconstruction from a single-RGB image in unconstrained real-world scenarios presents numerous challenges due to the inherent diversity and complexity of objects and environments. In this paper, we introduce Anything-3D, a methodical…

Computer Vision and Pattern Recognition · Computer Science 2023-04-21 Qiuhong Shen , Xingyi Yang , Xinchao Wang

Feed-forward 3D reconstruction for autonomous driving has advanced rapidly, yet existing methods struggle with the joint challenges of sparse, non-overlapping camera views and complex scene dynamics. We present UniSplat, a general…

Computer Vision and Pattern Recognition · Computer Science 2025-11-07 Chen Shi , Shaoshuai Shi , Xiaoyang Lyu , Chunyang Liu , Kehua Sheng , Bo Zhang , Li Jiang

Recovering the shape and appearance of real-world objects from natural 2D images is a long-standing and challenging inverse rendering problem. In this paper, we introduce a novel hybrid differentiable rendering method to efficiently…

Computer Vision and Pattern Recognition · Computer Science 2023-08-22 Xiangyang Zhu , Yiling Pan , Bailin Deng , Bin Wang

We introduce TransformerFusion, a transformer-based 3D scene reconstruction approach. From an input monocular RGB video, the video frames are processed by a transformer network that fuses the observations into a volumetric feature grid…

Computer Vision and Pattern Recognition · Computer Science 2021-07-07 Aljaž Božič , Pablo Palafox , Justus Thies , Angela Dai , Matthias Nießner

We present an approach for the planar surface reconstruction of a scene from images with limited overlap. This reconstruction task is challenging since it requires jointly reasoning about single image 3D reconstruction, correspondence…

Computer Vision and Pattern Recognition · Computer Science 2022-08-09 Samir Agarwala , Linyi Jin , Chris Rockwell , David F. Fouhey

Realistic scene reconstruction in driving scenarios poses significant challenges due to fast-moving objects. Most existing methods rely on labor-intensive manual labeling of object poses to reconstruct dynamic objects in canonical space and…

Computer Vision and Pattern Recognition · Computer Science 2025-09-03 Ruida Zhang , Chengxi Li , Chenyangguang Zhang , Xingyu Liu , Haili Yuan , Yanyan Li , Xiangyang Ji , Gim Hee Lee

We present a method for dynamic surface reconstruction of large-scale urban scenes from LiDAR. Depth-based reconstructions tend to focus on small-scale objects or large-scale SLAM reconstructions that treat moving objects as outliers. We…

Computer Vision and Pattern Recognition · Computer Science 2025-05-07 Nathaniel Chodosh , Anish Madan , Simon Lucey , Deva Ramanan

Realistic reconstruction of dynamic 4D scenes from monocular videos is essential for understanding the physical world. Despite recent progress in neural rendering, existing methods often struggle to recover accurate 3D geometry and…

Computer Vision and Pattern Recognition · Computer Science 2026-04-01 Haoran Zhou , Gim Hee Lee

Utilizing pre-trained 2D large-scale generative models, recent works are capable of generating high-quality novel views from a single in-the-wild image. However, due to the lack of information from multiple views, these works encounter…

Computer Vision and Pattern Recognition · Computer Science 2024-03-27 Yunhan Yang , Yukun Huang , Xiaoyang Wu , Yuan-Chen Guo , Song-Hai Zhang , Hengshuang Zhao , Tong He , Xihui Liu

Recent works have demonstrated that natural language can be used to generate and edit 3D shapes. However, these methods generate shapes with limited fidelity and diversity. We introduce CLIP-Sculptor, a method to address these constraints…

Computer Vision and Pattern Recognition · Computer Science 2023-05-25 Aditya Sanghi , Rao Fu , Vivian Liu , Karl Willis , Hooman Shayani , Amir Hosein Khasahmadi , Srinath Sridhar , Daniel Ritchie

Recent agentic frameworks for 3D scene synthesis have advanced realism and diversity by integrating heterogeneous generation and editing tools. These tools are organized into workflows orchestrated by an off-the-shelf LLM. Current…

Computer Vision and Pattern Recognition · Computer Science 2026-04-23 Yun He , Kelin Yu , Matthias Zwicker

In the last twenty years, Structure from Motion (SfM) has been a constant research hotspot in the fields of photogrammetry, computer vision, robotics etc., whereas real-time performance is just a recent topic of growing interest. This work…

Computer Vision and Pattern Recognition · Computer Science 2024-07-16 Zongqian Zhan , Yifei Yu , Rui Xia , Wentian Gan , Hong Xie , Giulio Perda , Luca Morelli , Fabio Remondino , Xin Wang

Semantic reconstruction of indoor scenes refers to both scene understanding and object reconstruction. Existing works either address one part of this problem or focus on independent objects. In this paper, we bridge the gap between…

Computer Vision and Pattern Recognition · Computer Science 2020-02-28 Yinyu Nie , Xiaoguang Han , Shihui Guo , Yujian Zheng , Jian Chang , Jian Jun Zhang

Achieving precise, object-level control in image editing remains challenging: 2D methods lack 3D awareness and often yield ambiguous or implausible results, while existing 3D-aware approaches rely on heavy optimization or incomplete…

Computer Vision and Pattern Recognition · Computer Science 2026-03-31 Yuhuan Xie , Aoxuan Pan , Yi-Hua Huang , Chirui Chang , Peng Dai , Xin Yu , Xiaojuan Qi

Generating 3D worlds from text is a highly anticipated goal in computer vision. Existing works are limited by the degree of exploration they allow inside of a scene, i.e., produce streched-out and noisy artifacts when moving beyond central…

Computer Vision and Pattern Recognition · Computer Science 2025-09-17 Manuel-Andreas Schneider , Lukas Höllein , Matthias Nießner

We present a framework that enables fast reconstruction and real-time rendering of urban-scale scenes while maintaining robustness against appearance variations across multi-view captures. Our approach begins with scene partitioning for…

Computer Vision and Pattern Recognition · Computer Science 2025-08-01 Zhensheng Yuan , Haozhi Huang , Zhen Xiong , Di Wang , Guanghua Yang

In recent years, 3D generation has made great strides in both academia and industry. However, generating 3D scenes from a single RGB image remains a significant challenge, as current approaches often struggle to ensure both object…

Graphics · Computer Science 2026-02-18 Xiang Tang , Ruotong Li , Xiaopeng Fan

Recovering 3D structure and camera motion from images has been a long-standing focus of computer vision research and is known as Structure-from-Motion (SfM). Solutions to this problem are categorized into incremental and global approaches.…

Computer Vision and Pattern Recognition · Computer Science 2024-09-24 Linfei Pan , Dániel Baráth , Marc Pollefeys , Johannes L. Schönberger

This paper tackles the challenging task of 3D visual grounding-locating a specific object in a 3D point cloud scene based on text descriptions. Existing methods fall into two categories: top-down and bottom-up methods. Top-down methods rely…

Computer Vision and Pattern Recognition · Computer Science 2024-10-22 Yang Liu , Daizong Liu , Wei Hu

Image-guided object assembly represents a burgeoning research topic in computer vision. This paper introduces a novel task: translating multi-view images of a structural 3D model (for example, one constructed with building blocks drawn from…

Computer Vision and Pattern Recognition · Computer Science 2024-04-26 Hongyu Yan , Yadong Mu